What do prediction/mean and label/mean represent in this TensorFlow code?
All of these could be problem specific (except maybe accuracy). Most of it is documented here:


accuracy: Percentage of correct number of classifications
accuracy_baseline: Accuracy baseline based on labels mean. This is the best the model could do by always predicting one class. (source)
AUC or Area Under the (ROC) Curve is quite complicated, but tells you something about the true/false positive rate. In short: the AUC is equal to the probability that a classifier will rank a randomly chosen positive instance higher than a randomly chosen negative one.
auc_precision_recall: Is the percentage of relevant intstances, among the retrieved instances, that have been retrieved over the total amount of relevant instances.
average_loss: You're usually minimizing some function, and this is likely the average value of that function given the current batches.
loss: The current value of the loss (as above). Either the sum of the losses, or the loss of the last batch.
global_step: Number of iterations.
label/mean and prediction/mean: Not really sure, but I suspect that if you have two classes then the label/mean is the mean of the value labels, whilst prediction/mean could be the value of the corresponding predictions. (two classes could give you a value between 0 and 1)